Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations731
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory91.5 KiB
Average record size in memory128.2 B

Variable types

Numeric10
DateTime1
Categorical5

Alerts

atemp is highly overall correlated with casual and 4 other fieldsHigh correlation
casual is highly overall correlated with atemp and 4 other fieldsHigh correlation
cnt is highly overall correlated with atemp and 5 other fieldsHigh correlation
hum is highly overall correlated with weathersitHigh correlation
instant is highly overall correlated with cnt and 3 other fieldsHigh correlation
mnth is highly overall correlated with seasonHigh correlation
registered is highly overall correlated with atemp and 5 other fieldsHigh correlation
season is highly overall correlated with atemp and 3 other fieldsHigh correlation
temp is highly overall correlated with atemp and 4 other fieldsHigh correlation
weathersit is highly overall correlated with humHigh correlation
weekday is highly overall correlated with workingdayHigh correlation
workingday is highly overall correlated with casual and 1 other fieldsHigh correlation
yr is highly overall correlated with cnt and 2 other fieldsHigh correlation
holiday is highly imbalanced (81.2%)Imbalance
instant is uniformly distributedUniform
instant has unique valuesUnique
dteday has unique valuesUnique
weekday has 105 (14.4%) zerosZeros

Reproduction

Analysis started2024-09-14 11:31:51.047115
Analysis finished2024-09-14 11:31:58.084216
Duration7.04 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

instant
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct731
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean366
Minimum1
Maximum731
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-09-14T13:31:58.133872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile37.5
Q1183.5
median366
Q3548.5
95-th percentile694.5
Maximum731
Range730
Interquartile range (IQR)365

Descriptive statistics

Standard deviation211.16581
Coefficient of variation (CV)0.57695577
Kurtosis-1.2
Mean366
Median Absolute Deviation (MAD)183
Skewness0
Sum267546
Variance44591
MonotonicityStrictly increasing
2024-09-14T13:31:58.248297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
731 1
 
0.1%
1 1
 
0.1%
2 1
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
Other values (721) 721
98.6%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
731 1
0.1%
730 1
0.1%
729 1
0.1%
728 1
0.1%
727 1
0.1%
726 1
0.1%
725 1
0.1%
724 1
0.1%
723 1
0.1%
722 1
0.1%

dteday
Date

UNIQUE 

Distinct731
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Minimum2011-01-01 00:00:00
Maximum2012-12-31 00:00:00
2024-09-14T13:31:58.311833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:58.395847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

season
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
3
188 
2
184 
1
181 
4
178 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters731
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 188
25.7%
2 184
25.2%
1 181
24.8%
4 178
24.4%

Length

2024-09-14T13:31:58.480283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-14T13:31:58.542813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 188
25.7%
2 184
25.2%
1 181
24.8%
4 178
24.4%

Most occurring characters

ValueCountFrequency (%)
3 188
25.7%
2 184
25.2%
1 181
24.8%
4 178
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 731
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 188
25.7%
2 184
25.2%
1 181
24.8%
4 178
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 731
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 188
25.7%
2 184
25.2%
1 181
24.8%
4 178
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 731
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 188
25.7%
2 184
25.2%
1 181
24.8%
4 178
24.4%

yr
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
1
366 
0
365 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters731
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 366
50.1%
0 365
49.9%

Length

2024-09-14T13:31:58.611897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-14T13:31:58.657817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 366
50.1%
0 365
49.9%

Most occurring characters

ValueCountFrequency (%)
1 366
50.1%
0 365
49.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 731
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 366
50.1%
0 365
49.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 731
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 366
50.1%
0 365
49.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 731
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 366
50.1%
0 365
49.9%

mnth
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5198358
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-09-14T13:31:58.711964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4519128
Coefficient of variation (CV)0.52944781
Kurtosis-1.209112
Mean6.5198358
Median Absolute Deviation (MAD)3
Skewness-0.0081486501
Sum4766
Variance11.915702
MonotonicityNot monotonic
2024-09-14T13:31:58.773661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 62
8.5%
3 62
8.5%
7 62
8.5%
5 62
8.5%
12 62
8.5%
10 62
8.5%
8 62
8.5%
4 60
8.2%
9 60
8.2%
6 60
8.2%
Other values (2) 117
16.0%
ValueCountFrequency (%)
1 62
8.5%
2 57
7.8%
3 62
8.5%
4 60
8.2%
5 62
8.5%
6 60
8.2%
7 62
8.5%
8 62
8.5%
9 60
8.2%
10 62
8.5%
ValueCountFrequency (%)
12 62
8.5%
11 60
8.2%
10 62
8.5%
9 60
8.2%
8 62
8.5%
7 62
8.5%
6 60
8.2%
5 62
8.5%
4 60
8.2%
3 62
8.5%

holiday
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
0
710 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters731
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 710
97.1%
1 21
 
2.9%

Length

2024-09-14T13:31:58.827571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-14T13:31:58.889693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 710
97.1%
1 21
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 710
97.1%
1 21
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 731
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 710
97.1%
1 21
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 731
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 710
97.1%
1 21
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 731
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 710
97.1%
1 21
 
2.9%

weekday
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.997264
Minimum0
Maximum6
Zeros105
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-09-14T13:31:58.927895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0047869
Coefficient of variation (CV)0.66887231
Kurtosis-1.2542824
Mean2.997264
Median Absolute Deviation (MAD)2
Skewness0.0027415977
Sum2191
Variance4.0191706
MonotonicityNot monotonic
2024-09-14T13:31:58.990341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 105
14.4%
0 105
14.4%
1 105
14.4%
2 104
14.2%
3 104
14.2%
4 104
14.2%
5 104
14.2%
ValueCountFrequency (%)
0 105
14.4%
1 105
14.4%
2 104
14.2%
3 104
14.2%
4 104
14.2%
5 104
14.2%
6 105
14.4%
ValueCountFrequency (%)
6 105
14.4%
5 104
14.2%
4 104
14.2%
3 104
14.2%
2 104
14.2%
1 105
14.4%
0 105
14.4%

workingday
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
1
500 
0
231 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters731
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 500
68.4%
0 231
31.6%

Length

2024-09-14T13:31:59.045216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-14T13:31:59.097897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
68.4%
0 231
31.6%

Most occurring characters

ValueCountFrequency (%)
1 500
68.4%
0 231
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 731
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 500
68.4%
0 231
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 731
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 500
68.4%
0 231
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 731
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 500
68.4%
0 231
31.6%

weathersit
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
1
463 
2
247 
3
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters731
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 463
63.3%
2 247
33.8%
3 21
 
2.9%

Length

2024-09-14T13:31:59.161335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-14T13:31:59.216409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 463
63.3%
2 247
33.8%
3 21
 
2.9%

Most occurring characters

ValueCountFrequency (%)
1 463
63.3%
2 247
33.8%
3 21
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 731
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 463
63.3%
2 247
33.8%
3 21
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 731
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 463
63.3%
2 247
33.8%
3 21
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 731
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 463
63.3%
2 247
33.8%
3 21
 
2.9%

temp
Real number (ℝ)

HIGH CORRELATION 

Distinct499
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49538479
Minimum0.0591304
Maximum0.861667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-09-14T13:31:59.286148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0591304
5-th percentile0.2135685
Q10.3370835
median0.498333
Q30.6554165
95-th percentile0.76875
Maximum0.861667
Range0.8025366
Interquartile range (IQR)0.318333

Descriptive statistics

Standard deviation0.183051
Coefficient of variation (CV)0.36951275
Kurtosis-1.1188642
Mean0.49538479
Median Absolute Deviation (MAD)0.158333
Skewness-0.054520965
Sum362.12628
Variance0.033507667
MonotonicityNot monotonic
2024-09-14T13:31:59.361005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.265833 5
 
0.7%
0.635 5
 
0.7%
0.564167 4
 
0.5%
0.4375 4
 
0.5%
0.484167 4
 
0.5%
0.696667 4
 
0.5%
0.649167 4
 
0.5%
0.710833 4
 
0.5%
0.68 4
 
0.5%
0.521667 3
 
0.4%
Other values (489) 690
94.4%
ValueCountFrequency (%)
0.0591304 1
0.1%
0.0965217 1
0.1%
0.0973913 1
0.1%
0.1075 1
0.1%
0.1275 1
0.1%
0.134783 1
0.1%
0.138333 1
0.1%
0.144348 1
0.1%
0.15 1
0.1%
0.150833 1
0.1%
ValueCountFrequency (%)
0.861667 1
0.1%
0.849167 1
0.1%
0.848333 1
0.1%
0.838333 1
0.1%
0.834167 1
0.1%
0.83 1
0.1%
0.828333 1
0.1%
0.8275 1
0.1%
0.8225 1
0.1%
0.818333 1
0.1%

atemp
Real number (ℝ)

HIGH CORRELATION 

Distinct690
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47435399
Minimum0.0790696
Maximum0.840896
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-09-14T13:31:59.432291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0790696
5-th percentile0.2206455
Q10.3378425
median0.486733
Q30.608602
95-th percentile0.714967
Maximum0.840896
Range0.7618264
Interquartile range (IQR)0.2707595

Descriptive statistics

Standard deviation0.16296118
Coefficient of variation (CV)0.34354339
Kurtosis-0.98513053
Mean0.47435399
Median Absolute Deviation (MAD)0.135624
Skewness-0.13108804
Sum346.75277
Variance0.026556346
MonotonicityNot monotonic
2024-09-14T13:31:59.521018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.654688 4
 
0.5%
0.637008 3
 
0.4%
0.375621 3
 
0.4%
0.594083 2
 
0.3%
0.378779 2
 
0.3%
0.243058 2
 
0.3%
0.607962 2
 
0.3%
0.272721 2
 
0.3%
0.542929 2
 
0.3%
0.387608 2
 
0.3%
Other values (680) 707
96.7%
ValueCountFrequency (%)
0.0790696 1
0.1%
0.0988391 1
0.1%
0.101658 1
0.1%
0.116175 1
0.1%
0.11793 1
0.1%
0.119337 1
0.1%
0.126275 1
0.1%
0.144283 1
0.1%
0.149548 1
0.1%
0.150883 1
0.1%
ValueCountFrequency (%)
0.840896 1
0.1%
0.826371 1
0.1%
0.804913 1
0.1%
0.804287 1
0.1%
0.794829 1
0.1%
0.790396 1
0.1%
0.786613 1
0.1%
0.785967 1
0.1%
0.761367 1
0.1%
0.757579 1
0.1%

hum
Real number (ℝ)

HIGH CORRELATION 

Distinct595
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62789406
Minimum0
Maximum0.9725
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-09-14T13:31:59.601581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4074545
Q10.52
median0.626667
Q30.7302085
95-th percentile0.8685415
Maximum0.9725
Range0.9725
Interquartile range (IQR)0.2102085

Descriptive statistics

Standard deviation0.1424291
Coefficient of variation (CV)0.22683619
Kurtosis-0.064530135
Mean0.62789406
Median Absolute Deviation (MAD)0.104584
Skewness-0.069783434
Sum458.99056
Variance0.020286047
MonotonicityNot monotonic
2024-09-14T13:31:59.676159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.613333 4
 
0.5%
0.538333 3
 
0.4%
0.57 3
 
0.4%
0.605 3
 
0.4%
0.552083 3
 
0.4%
0.568333 3
 
0.4%
0.590417 3
 
0.4%
0.722917 3
 
0.4%
0.630833 3
 
0.4%
0.5425 3
 
0.4%
Other values (585) 700
95.8%
ValueCountFrequency (%)
0 1
0.1%
0.187917 1
0.1%
0.254167 1
0.1%
0.275833 1
0.1%
0.29 1
0.1%
0.302174 1
0.1%
0.305 1
0.1%
0.31125 1
0.1%
0.314167 1
0.1%
0.314348 1
0.1%
ValueCountFrequency (%)
0.9725 1
0.1%
0.970417 1
0.1%
0.9625 1
0.1%
0.949583 1
0.1%
0.948261 1
0.1%
0.939565 1
0.1%
0.93 1
0.1%
0.929167 1
0.1%
0.925 1
0.1%
0.9225 1
0.1%

windspeed
Real number (ℝ)

Distinct650
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19048621
Minimum0.0223917
Maximum0.507463
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-09-14T13:31:59.744875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0223917
5-th percentile0.07961665
Q10.13495
median0.180975
Q30.2332145
95-th percentile0.343283
Maximum0.507463
Range0.4850713
Interquartile range (IQR)0.0982645

Descriptive statistics

Standard deviation0.077497871
Coefficient of variation (CV)0.40684242
Kurtosis0.41092227
Mean0.19048621
Median Absolute Deviation (MAD)0.049129
Skewness0.67734542
Sum139.24542
Variance0.00600592
MonotonicityNot monotonic
2024-09-14T13:31:59.817831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.136817 3
 
0.4%
0.149883 3
 
0.4%
0.167912 3
 
0.4%
0.228858 3
 
0.4%
0.10635 3
 
0.4%
0.134954 3
 
0.4%
0.118792 3
 
0.4%
0.166667 3
 
0.4%
0.1107 3
 
0.4%
0.236321 2
 
0.3%
Other values (640) 702
96.0%
ValueCountFrequency (%)
0.0223917 1
0.1%
0.0423042 1
0.1%
0.0454042 1
0.1%
0.0454083 1
0.1%
0.04665 1
0.1%
0.047275 1
0.1%
0.0503792 1
0.1%
0.0528708 1
0.1%
0.053213 1
0.1%
0.057225 1
0.1%
ValueCountFrequency (%)
0.507463 1
0.1%
0.441563 1
0.1%
0.422275 1
0.1%
0.421642 1
0.1%
0.417908 1
0.1%
0.415429 1
0.1%
0.4148 1
0.1%
0.409212 1
0.1%
0.407346 1
0.1%
0.398008 1
0.1%

casual
Real number (ℝ)

HIGH CORRELATION 

Distinct606
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean848.17647
Minimum2
Maximum3410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-09-14T13:31:59.889448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile88
Q1315.5
median713
Q31096
95-th percentile2355
Maximum3410
Range3408
Interquartile range (IQR)780.5

Descriptive statistics

Standard deviation686.62249
Coefficient of variation (CV)0.80952787
Kurtosis1.3220743
Mean848.17647
Median Absolute Deviation (MAD)396
Skewness1.266454
Sum620017
Variance471450.44
MonotonicityNot monotonic
2024-09-14T13:31:59.962627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
968 4
 
0.5%
120 4
 
0.5%
244 3
 
0.4%
163 3
 
0.4%
653 3
 
0.4%
140 3
 
0.4%
775 3
 
0.4%
123 3
 
0.4%
639 3
 
0.4%
150 2
 
0.3%
Other values (596) 700
95.8%
ValueCountFrequency (%)
2 1
0.1%
9 2
0.3%
15 1
0.1%
25 1
0.1%
34 1
0.1%
38 2
0.3%
41 1
0.1%
42 1
0.1%
43 1
0.1%
46 1
0.1%
ValueCountFrequency (%)
3410 1
0.1%
3283 1
0.1%
3252 1
0.1%
3160 1
0.1%
3155 1
0.1%
3065 1
0.1%
3031 1
0.1%
2963 1
0.1%
2855 1
0.1%
2846 1
0.1%

registered
Real number (ℝ)

HIGH CORRELATION 

Distinct679
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3656.1724
Minimum20
Maximum6946
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-09-14T13:32:00.035717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile1177.5
Q12497
median3662
Q34776.5
95-th percentile6280.5
Maximum6946
Range6926
Interquartile range (IQR)2279.5

Descriptive statistics

Standard deviation1560.2564
Coefficient of variation (CV)0.42674585
Kurtosis-0.71309714
Mean3656.1724
Median Absolute Deviation (MAD)1155
Skewness0.04365878
Sum2672662
Variance2434400
MonotonicityNot monotonic
2024-09-14T13:32:00.112213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1707 3
 
0.4%
6248 3
 
0.4%
4841 3
 
0.4%
4240 2
 
0.3%
3578 2
 
0.3%
1628 2
 
0.3%
3425 2
 
0.3%
3848 2
 
0.3%
3107 2
 
0.3%
3614 2
 
0.3%
Other values (669) 708
96.9%
ValueCountFrequency (%)
20 1
0.1%
416 1
0.1%
432 1
0.1%
451 1
0.1%
472 1
0.1%
491 1
0.1%
570 1
0.1%
573 1
0.1%
577 1
0.1%
654 1
0.1%
ValueCountFrequency (%)
6946 1
0.1%
6917 1
0.1%
6911 1
0.1%
6898 1
0.1%
6844 1
0.1%
6820 1
0.1%
6803 1
0.1%
6790 1
0.1%
6781 1
0.1%
6750 1
0.1%

cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct696
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4504.3488
Minimum22
Maximum8714
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-09-14T13:32:00.393338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile1331
Q13152
median4548
Q35956
95-th percentile7576
Maximum8714
Range8692
Interquartile range (IQR)2804

Descriptive statistics

Standard deviation1937.2115
Coefficient of variation (CV)0.4300758
Kurtosis-0.81192238
Mean4504.3488
Median Absolute Deviation (MAD)1407
Skewness-0.04735278
Sum3292679
Variance3752788.2
MonotonicityNot monotonic
2024-09-14T13:32:00.462299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4758 2
 
0.3%
4073 2
 
0.3%
2424 2
 
0.3%
4098 2
 
0.3%
1096 2
 
0.3%
5202 2
 
0.3%
3974 2
 
0.3%
5312 2
 
0.3%
3351 2
 
0.3%
4401 2
 
0.3%
Other values (686) 711
97.3%
ValueCountFrequency (%)
22 1
0.1%
431 1
0.1%
441 1
0.1%
506 1
0.1%
605 1
0.1%
623 1
0.1%
627 1
0.1%
683 1
0.1%
705 1
0.1%
754 1
0.1%
ValueCountFrequency (%)
8714 1
0.1%
8555 1
0.1%
8395 1
0.1%
8362 1
0.1%
8294 1
0.1%
8227 1
0.1%
8173 1
0.1%
8167 1
0.1%
8156 1
0.1%
8120 1
0.1%

Interactions

2024-09-14T13:31:57.344144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:51.415144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:52.055393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:52.662972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:53.624931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:54.212089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:54.812057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:55.396744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:55.978036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:56.761004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:57.394170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:51.480001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:52.119951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:52.744808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:53.679048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:54.277440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:54.861701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:55.464815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:56.047012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:56.810865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:57.454245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:51.546130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:52.182053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:52.816596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:53.731040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:54.347388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:54.927882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:55.511717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:56.094851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:56.879659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:57.513255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:51.599856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:52.248528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:52.876691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:53.800517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:54.395412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:54.986169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:55.579857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:56.151115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:56.927506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:57.576427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:51.672455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:52.312952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:52.929164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:53.866573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:54.465603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:55.065562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:55.648913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:56.210503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:56.996805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:57.627400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:51.741730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:52.381454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:52.986986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:53.932370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:54.528001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:55.111743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:55.711108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:56.297695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:57.053817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:57.677282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:51.796493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:52.429597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:53.045988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:53.979023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:54.578933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:55.178585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:55.761693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:56.363035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:57.113092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:57.729698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:51.863014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:52.485131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:53.445998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:54.044419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:54.645212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:55.228615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:55.811573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:56.411341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:57.160998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:57.777275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:51.929167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:52.549686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:53.514706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:54.095471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:54.695298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:55.298561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:55.878329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:56.477692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:57.227502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:57.843856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:51.997342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:52.614249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:53.562509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:54.165933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:54.761761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:55.347850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:55.930424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:56.710269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T13:31:57.277413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-14T13:32:00.531073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
atempcasualcntholidayhuminstantmnthregisteredseasontempweathersitweekdaywindspeedworkingdayyr
atemp1.0000.6680.6230.0220.1400.1420.2090.5320.5770.9930.170-0.013-0.1690.0880.082
casual0.6681.0000.7540.036-0.0710.3130.1850.5240.3740.6670.2210.040-0.1800.5710.317
cnt0.6230.7541.0000.092-0.0980.6300.2720.9400.3790.6220.3340.064-0.2170.1520.640
holiday0.0220.0360.0921.0000.0000.0000.0000.0680.0000.0000.0000.2680.0000.2420.000
hum0.140-0.071-0.0980.0001.0000.0100.213-0.0930.1510.1300.551-0.054-0.2390.0550.152
instant0.1420.3130.6300.0000.0101.0000.4970.6640.8000.1420.119-0.000-0.1300.0000.992
mnth0.2090.1850.2720.0000.2130.4971.0000.2840.8840.2080.1040.009-0.2070.0000.000
registered0.5320.5240.9400.068-0.0930.6640.2841.0000.3610.5310.3080.058-0.2030.3760.647
season0.5770.3740.3790.0000.1510.8000.8840.3611.0000.5720.0780.0000.1900.0000.000
temp0.9930.6670.6220.0000.1300.1420.2080.5310.5721.0000.148-0.004-0.1470.0410.112
weathersit0.1700.2210.3340.0000.5510.1190.1040.3080.0780.1481.0000.0400.1100.0320.054
weekday-0.0130.0400.0640.268-0.054-0.0000.0090.0580.000-0.0040.0401.0000.0130.9350.000
windspeed-0.169-0.180-0.2170.000-0.239-0.130-0.207-0.2030.190-0.1470.1100.0131.0000.0450.062
workingday0.0880.5710.1520.2420.0550.0000.0000.3760.0000.0410.0320.9350.0451.0000.000
yr0.0820.3170.6400.0000.1520.9920.0000.6470.0000.1120.0540.0000.0620.0001.000

Missing values

2024-09-14T13:31:57.911782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-14T13:31:58.035624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

instantdtedayseasonyrmnthholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
012011-01-0110106020.3441670.3636250.8058330.160446331654985
122011-01-0210100020.3634780.3537390.6960870.248539131670801
232011-01-0310101110.1963640.1894050.4372730.24830912012291349
342011-01-0410102110.2000000.2121220.5904350.16029610814541562
452011-01-0510103110.2269570.2292700.4369570.1869008215181600
562011-01-0610104110.2043480.2332090.5182610.0895658815181606
672011-01-0710105120.1965220.2088390.4986960.16872614813621510
782011-01-0810106020.1650000.1622540.5358330.26680468891959
892011-01-0910100010.1383330.1161750.4341670.36195054768822
9102011-01-1010101110.1508330.1508880.4829170.2232674112801321
instantdtedayseasonyrmnthholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
7217222012-12-22111206010.2658330.2361130.4412500.40734620515441749
7227232012-12-23111200010.2458330.2594710.5154170.13308340813791787
7237242012-12-24111201120.2313040.2589000.7913040.077230174746920
7247252012-12-25111212020.2913040.2944650.7347830.1687264405731013
7257262012-12-26111203130.2433330.2203330.8233330.3165469432441
7267272012-12-27111204120.2541670.2266420.6529170.35013324718672114
7277282012-12-28111205120.2533330.2550460.5900000.15547164424513095
7287292012-12-29111206020.2533330.2424000.7529170.12438315911821341
7297302012-12-30111200010.2558330.2317000.4833330.35075436414321796
7307312012-12-31111201120.2158330.2234870.5775000.15484643922902729